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Python 3 Text Processing with NLTK 3 Cookbook

You're reading from   Python 3 Text Processing with NLTK 3 Cookbook

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Product type Paperback
Published in Aug 2014
Publisher
ISBN-13 9781782167853
Length 304 pages
Edition 2nd Edition
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Author (1):
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Jacob Perkins Jacob Perkins
Author Profile Icon Jacob Perkins
Jacob Perkins
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Table of Contents (12) Chapters Close

Preface 1. Tokenizing Text and WordNet Basics FREE CHAPTER 2. Replacing and Correcting Words 3. Creating Custom Corpora 4. Part-of-speech Tagging 5. Extracting Chunks 6. Transforming Chunks and Trees 7. Text Classification 8. Distributed Processing and Handling Large Datasets 9. Parsing Specific Data Types A. Penn Treebank Part-of-speech Tags
Index

Parallel list processing with execnet


This recipe presents a pattern for using execnet to process a list in parallel. It's a function pattern for mapping each element in the list to a new value, using execnet to do the mapping in parallel.

How to do it...

First, we need to decide exactly what we want to do. In this example, we'll just double integers, but we could do any pure computation. Following is the remote_double.py module, which will be executed by execnet. It receives a 2-tuple of (i, arg), assumes arg is a number, and sends back (i, arg*2). The need for i will be explained in the next section.

if __name__ == '__channelexec__':
  for (i, arg) in channel:
    channel.send((i, arg * 2))

To use this module to double every element in a list, we import the plists module (explained in the How it works... section) and call plists.map() with the remote_double module, and a list of integers to double.

>>> import plists, remote_double
>>> plists.map(remote_double, range(10))
...
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